Package: Source: https://github.com/rladies/meetupr

Sys.setenv(MEETUP_KEY = "PASTE YOUR MEETUP KEY HERE")
# Slow function so we don't hit the Meetup rate limit From here:
# https://github.com/rladies/meetupr/issues/30#issuecomment-379900167
slowly <- function(f, delay = 0.25) {
    function(...) {
        Sys.sleep(delay)
        f(...)
    }
}
# Wrap 'get_events'' to link the parent group into the result data
get_group_events <- function(group) {
    events <- get_events(group, event_status = "past")
    events <- events %>% mutate(group_url = group)
}

unique_groups <- read.csv("groups.csv")

# Split the requests into chunks in case we disconnect part way 
chunk_size <- 100
number_chunks <- as.integer(nrow(unique_groups) / chunk_size) + 1

# Warning: This will take approximate 18 years 
for (offset in 1:number_chunks) {
  start <- 1 + (offset * chunk_size)
  end <- (offset + 1) * chunk_size
  print(offset)
    
  # Get the groups in the current chunk
  group_urls <- unique_groups %>%
    select(urlname) %>%
    slice(start:end) 
  
  # Request all the events for each group 
  event_results <- map(group_urls$urlname, slowly(safely(get_group_events)))
  
  # Guard against the end of the list 
  if (length(event_results) > 0) {
    filtered_results <- event_results %>%
      map("result") %>%
      bind_rows() %>%
      select(id, name, local_date, yes_rsvp_count, group_url)
  
    # Save our progress to disk in case we error out somewhere
    write_csv(filtered_results, path = "events.csv", append = TRUE)
  }
}

When making maps, sometimes you have data that has 2-character, 3-character abbreviations, full spelling. There's a handy package called rworldmaps that allows you to convert almost any geographic spelling to maps other types. See ?codelist for all options.

# Match country names in survey to the country names in package
matched = joinCountryData2Map(tech_jobs, joinCode="NAME", nameJoinColumn="country", verbose = T)

## 41 codes from your data successfully matched countries in the map
## 0 codes from your data failed to match with a country code in the map
##      failedCodes failedCountries
## 202 codes from the map weren't represented in your data

Create a data frame of the number of groups, by year and country We'll only keep 2016 since the data for tech jobs is cross-sectional Convert the 2-letter ios2c country codes to full length country names Merge the tech jobs & meetup tech groups!

groups_jobs = groups %>% 
        filter(year == 2016) %>% 
        group_by(country) %>%
        summarise(groups.total = n()) %>% 
        rename(country.abb = country) %>% 
        ungroup()

groups_jobs$country = countrycode(groups_jobs$country.abb, "iso2c", "country.name")
        
groups_jobs %<>%  left_join(tech_jobs) %>% 
        na.omit() %>% data.frame()

plot.groups = ggplot(data = groups_jobs, aes(x = groups.total, y = tech_perc_women)) +
        geom_point() + 
        geom_text_repel(aes(label=country.abb), size = 3) + 
        labs(
                title = "Number of Women's Tech Groups and Women in Tech Jobs, by Country ",
                caption = "Source: https://www.honeypot.io/women-in-tech-2018/",
                x = "Women's Tech Meetup gGroups",
                y = "Percentage of women in tech") +
        guides(size = FALSE) +
        theme_bw() +
        theme(panel.grid.major.x = element_blank(),
        legend.position = 'none')